The existing automatic classification methods of aviation safety reports are mainly traditional machine learning classification algorithms, which have the disadvantages of relying on manual feature extraction and selection, unable to consider domain information and dealing with complex dependencies, which limits the improvement of classification accuracy. Therefore, this paper proposes an automatic classification algorithm for aviation safety reports that combines text and knowledge graph. The algorithm uses the knowledge triples with rich relationships as the background knowledge in the aviation safety field into the word2vec word vector training process so that the words can not only learn context information in training but also learn the complex semantics between words in the aviation field information, so that the trained word vectors can express richer semantic features, thereby improving the classification effect. Through a series of comparative experiments, the results show that the algorithm has the highest F1-score value and has a classification accuracy rate of up to 91.4%.